| Summary: | The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM<sub>2.5</sub>), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM<sub>2.5</sub> varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM<sub>2.5</sub> levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (<i>R</i>) of ~0.57 and a root mean square error (<i>RMSE</i>) of ~7.00 μg m<sup>−3</sup>. ENN performed better than EMLR, with an <i>R</i> of ~0.78 and <i>RMSE</i> of ~5.43 μg m<sup>−3</sup>. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM<sub>2.5</sub> distributions in an urban area with low air quality monitor density.
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